AIC values are the criteria that indicate the degree to which the observed data corresponds to a model. The residual sum of squares (RSS) becomes smaller as the number of parameters included in a model increases.

Hence, SNPAlyze not only compares the size of the RSS but also considers the number of parameters. Consequently, a model that leads to the minimum AIC is considered the best.

The first term is called the maximum likelihood of the model AIC and is a measure of how well the model fits the data. The second term is called the number of free parameters in the AIC of the model and indicates the penalty associated with the addition of parameters (and hence model complexity).

The determination reliability becomes higher as the absolute value of the AIC (in this case, it indicates the difference between a dependent and an independent model) increases. The absolute value of the AIC that is close to zero is considered equivalent to the 5% level of significance in the chi-square test, although this evaluation depends on the degree of freedom in the contingency table.